EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS Publisher: Asian Economic and Social Society ISSN (P): 2304-1455, ISSN (E): 2224-4433 Volume 3 No. 3 March 2013. † Corresponding author Relative Efficiency of Small and Medium Scale Agribusiness Enterprises in Imo State, Nigeria. Emesowum, Charles Emeka † (Department of Agricultural Economics and Extension Federal University Wukari PMB, Wukari, Taraba State, Nigeria) Mbanasor, Jude. A. (Department of Agribusiness and Management Michael Okpara University of Agriculture, Umudike, Umuahia Abia State, Nigeria) Citation: Emesowum, Charles Emeka and Mbanasor, Jude. A. (2013) “Relative Efficiency of Small and Medium Scale Agribusiness Enterprises in Imo State, Nigeria”, Asian Journal of Agriculture and Rural Development, Vol. 3, No. 3, pp. 127-134. Relative Efficiency of Small and Medium ... 127 Author(s) Emesowum, Charles Emeka Department of Agricultural Economics and Extension Federal University Wukari PMB, Wukari, Taraba State, Nigeria Mbanasor, Jude. A. Department of Agribusiness and Management Michael Okpara University of Agriculture, Umudike, Umuahia Abia State, Nigeria Relative Efficiency of Small and Medium Scale Agribusiness Enterprises in Imo State, Nigeria Abstract The study examined the Relative Efficiency of Small and Medium Scale Agribusiness Enterprises in Imo state, Nigeria using the stochastic translog profit function approach. A multi-stage and simple random sampling technique were used in selecting four hundred eighty enterprises (240 each from small and medium scale) from two Agricultural Zones of the state namely Orlu and Owerri zones. Economic efficiency was analysed using the stochastic translog profit function and Maximum Likelihood Estimation (MLE) technique was employed to estimate the function. Full economic efficiency was tested using generalized Likelihood Ratio (LR) and t-test statistic was employed to compare the small and medium scale enterprises in the area. The result depicted that both enterprises were not efficient but small scale enterprise was more efficient than medium scale enterprise. There was no significant difference in mean efficiency between the small and medium scale enterprises. The economic efficiencies of the enterprises varied widely between 0.09 and 0.93 and 0.08 and 0.91 respectively for small and medium scale enterprises with a mean of 0.57 and 0.54. Access to credit and business experience was found influencing economic efficiency in both small scale and medium scale enterprises. The study observed that there was an opportunity for increase in enterprises’ efficiency and small and medium scale enterprises should focus more on ways of accessing credit facilities. Keywords: Relative Efficiency, Economic Efficiency, Small and medium Scale Enterprise, Agribusiness, Profit Function, Translog Introduction In Nigeria, agriculture has always played a vital role in economic development over the past several decades which accounted for 88% of non-oil foreign exchange earnings and 70% of the active labour force of the population (CBN, 2000). Despite the enormous contribution of the sector to the Nigerian economy over the years, the sector has slipped into a system decline; particularly in the past three decades since the petroleum industry assume greater importance with poor agricultural development in the country (Opara, 2008). Although, overall agricultural enterprise productivity rose by 28% during the 1990’s, per capita output rose by only 8.5% during the same period (Library of Congress, 2006). But reports from FAO (2003) revealed that food supply has not kept the pace with demand. Achieving efficiency in agribusiness production has been the priority goals of many African Governments. But the agribusiness enterprise have not performed efficiently as a result of so many socio – economic, political constraint and other problems militating against performances such as; food security and food self reliance are serious challenges facing some of these economies. World Bank survey (1981) showed that inefficiency of agribusiness enterprises in most nations like Nigeria was as a result of the public policy structure that did not provide the right incentive for growth. According to IFC, (2003) the small and medium enterprise employ four to fifty workers. Asian Journal of Agriculture and Rural Development, 3(3): 127-134 128 Research has received minimal attention on efficiency with respect to agribusiness enterprises. The problem of economic efficiency in the utilization of resources has been the greatest concern of agribusiness entrepreneurs (Awoke and Okorji, 2003). This, study aims at analyzing and compares the economic efficiency of small and medium scale agribusiness enterprises in Imo State. Methodology Study Area The study was conducted in Imo State, specifically, Orlu and Owerri agricultural zones. The area lies between latitude of 5.2 o N and 6.08 0 N and longitude of 6.6 0 E and 7.5 0 E. The area has tropical climate characterized by high rainfall and temperature range of 1500mm- 2000mm and 34 0c -37 0c respectively. Agriculture is the major occupation of people and the major arable crops cultivated in this area include cassava, yam, cocoyam, maize, pepper, and other vegetables. The plantation crops such as oil palms, coconuts, rubber, cocoa, plantain and bananas. Livestock reared in Imo State include poultry, goat and sheep. Two out of three agricultural zones were purposively selected for the study. They are Orlu and Owerri zones. A multistage sampling technique was adopted for the study. Four Local Government Areas were purposively selected from two zones and ten small and medium scale agribusiness enterprises were purposively selected per LGA. The enterprises considered in the study in include cassava, feed and palm oil processing enterprises. 240 each of small and medium scale enterprises were selected for the study, making a total 480 enterprises. Data were analyzed using descriptive statistics such as frequencies, percentages, means and t-test. Model Specification The normalized translog profit function model was used to estimate the economic efficiency in small and medium scale enterprises. This can be specified as follows П* = П/p = F*i (k1; Z) (1) Where П= normalized profit of the ith enterprise k1 = vector of variable input prices Z = vector of fixed input prices Alternatively, the above equation can be written in transcendental logarithmic form as stated below InПE = βo + β1Ink1 + β2Ink2 + β3Ink3 + β4Ink4 + β5Ink5+ 0.5β6Ink1 2 + 0.5β7Ink2 2 + 0.5β8Ink3 2 + 0.5β9Ink4 2 + 0.5β10Ink5 2 + 0.5β11Ink1Ink2 +0.5β12Ink1Ink3+ 0.5β13Ink1Ink4 + 0.5β14Ink1Ink5+ 0.5β15Ink2Ink3 + 0.5β16Ink2Ink4 + 0.5β17Ink2Ink5+ 0.5β18Ink3Ink4 + 0.5β19Ink3Ink5 + 0.5β20Ink4Ink5 +Vi-Ui (2) Where ∏E = normalized profit in Naira per enterprise k1 = wage rate normalized by the price of output per enterprise k2 = price of other inputs normalized by the price of output per enterprise k3 = price of petroleum/fuel used normalized by the price of output per enterprise k4 = Unit cost of transportation normalized by the price of output per enterprise k5 = capita inputs (interest rate) Naira. U1=error term under the control of the enterprise V1 =error term not under the control of the enterprises β0=intercept β1-β20= estimated coefficients The determinants of economic efficiency, Ui is defined by Exp (-Ui)] = bo+b1Z1 + b2Z2 + b3Z3 + b4Z4 + b5Z5 + b6Z6 + b7Z7 + ε (3) Where Exp (-Ui)] =Efficiency of the ith enterprise Zi = Age of the enterprise (in years) Z2 = Labour (in man-days) Z3 = credit status (Access = 1, No access = 0) Z4 = Business Experience (in years) Z5 = Membership of cooperative society (member = 1, non = 0) Z6 = Number of employees Z7 = Extension visit (number of times) ε = Error terms β and bs are scalar parameters that were estimated. To estimate the model and separate inefficiency (Ui) some assumption i.e. N (0, σ 2 v) while Ui has a half normal distribution i.e. Ui = (0, σ 2 v). The estimates for all the parameters of the stochastic frontier function and the inefficiency were simultaneously obtained, Relative Efficiency of Small and Medium ... 129 using the computer program frontier version 4.1(Coelli, 1996). Tests of null hypothesis on efficiency was carried out using the generalized likelihood ratio (LR) test statistic which is defined by λ = -2 ln [L(Ho)/2(H1)] Where L(Ho) is the value of the likelihood function for the frontier model, which the parameter restrictions specified by the null hypothesis, Ho, are imposed; and H1 is the value of the likelihood function for the general frontier model. The test statistic LR (λ) has a chi-square (Χ 2 ) distribution which has a degree of freedom equal to q+1, where q is equal to the number of parameters involved in Ho and H1 (Spilaimen and Lansink, 2005). If the null hypothesis is true then λ has approximately chi- square (or mixed square) distribution with degrees of freedom equal to the difference between the parameters under Hi and Ho, respectively. The efficiency indices were compared using a t-test as stated below tcal = X1 – X2 √ S 2 1 + S2 2 n1 n2 Where X1 = the mean economic efficiency indices of small scale enterprises X2 = the mean economic efficiency indices of medium scale enterprises S1 2 = the variance economic efficiency indices of small scale enterprises S2 2 = the variance of economic efficiency indices of medium scale enterprises n1 = the number of sampled small scale enterprises n2 = the number of sampled medium scale enterprises Results and Discussion Estimation Economic Efficiency Table 1 depicts the maximum likelihood estimates of the profit frontier function of small scale agribusiness enterprises in Imo State. The sigma square (δ 2 ) indicate the goodness of fit and correctness of the specified assumption of the composite error terms distribution (Idiong, 2005 and Okoye, 2006). The variance ratio (γ = 0.98) indicating that 98% of variation in the total profit is due to inefficiency that 98% of variation in the total profit is due to inefficiency. The result shows that all the variables are significant except capital inputs (interest) rate that is not significant even at 10% level of probability. Coefficient of wage rate is positively signed and significant at 1% probability level. This implies that wage is increasing with profit; this explains the positive impact of wage on the profit structure of small scale enterprises which agrees with Ajibefun and Daramola, (2003). Price of other inputs, petrol and unit cost of transportation showed negative relationship with profit of the enterprise. This indicates that every 1% increase in price of other inputs, petrol and unit cost of transportation would lead to 1.459, 3.401 and 11.498percent reduction in profitability of the enterprise. The result of medium scale enterprise in table 2 revealed that coefficients of wage rate, price of other inputs, petrol and cost of transportation are statistically significant but wage rate is positively signed while price of other inputs, petrol and transportation cost are negatively signed which agreed with the a priori expectations. This implies that price of other inputs, petrol and transportation costs are decreasing with profit while wage rate is increasing with profit with the tune of 15.521%. The diagnostic statistics have coefficients that are highly significant at 1% level of probability. The coefficient for total variance (δ 2 ) is 0.107 indicating good fit while variance ratio of 0.963. This would mean that 96.3% of the variation in profit among the medium scale enterprise is due to economic inefficiency. Comparing the two enterprises, small scale profit function result, revealed that the coefficient of wage rate (positive) price of other inputs, and petrol were statistically significant at 1% while transportation cost is significant at 5% level of probability. The variance parameter had a value of 0.45 and log-likelihood function of -233.090 whereas the medium scale enterprise showed that wage rate was positive and significant at 5% probability level, price of other inputs, transportation cost and petrol were negative, and significant at 1% level except transportation cost that is significant at 10% level. The variance ratio was 0.107 while the Asian Journal of Agriculture and Rural Development, 3(3): 127-134 130 log-likelihood stood at -333.083. Based on a high value of the log-likelihood ratio and the variance parameter, the small scale enterprise was more efficient than medium scale enterprises in Imo state. This result is consistent with findings of Sanusi, (2003), Amaechi, (2007) and Owualah, (1999) which admitted that small scale enterprises are more efficient, and enjoy a competitive advantage over medium and large scale enterprises. Economic Efficiency Analysis Although economic efficiency estimates presented in table 3 indicated a range of 0.98 to 0.09 for small scale enterprises and 0.91 and 0.08 for medium scale respectively; the mean economic efficiency was 0.57 and 0.54 for small and medium scale enterprises respectively. The estimates show that for the average small and medium scale agribusiness enterprise to attain the level of the most economical efficient farmer in the sample, the enterprise would maximize a profit if 38.71% (1-0.57/0.93) for small scale and 40.66% (1- 0.54/0.91) for medium scale enterprises. The least economically efficient enterprise will have an efficiency gain of 90.32% (1-0.09/0.93) for small scale and 91.21% (1-0.08/0.91) for medium scale enterprise respectively, if the enterprise is to attain the efficiency level of most economically efficient agro-processing enterprise in the study area. This result further suggests that there are still opportunities to increase profitability through increased efficiency in resource utilization by both levels of enterprises in Imo State. A comparative analysis was equally carried out to ascertain the difference in economic efficiency between small and medium scale agribusiness enterprises. The result showed that there was no significant difference in the mean of economic efficiency between the two enterprises in the state. The t-calculated for economic efficiency was 0.838 respectively and where less than the t-critical value of 10% (tα0.1 = 1.282). This implies that the SMEs share similar features and use almost the same kind production. The only difference might be the amount of capital employed. Determinant of Economic Efficiency The coefficient (table 4) of labour, credit status, business experience and number of employees are statistically significant which agreed with the a priori expectation. However, labour and number of employee are significant at 1%negatively signed. The result implies that the addition of labour and number of employees, lower the profit of the enterprise. The coefficient of business experience and credit status are positively signed, implying that the more experienced and access to credit an enterprise has, the high the level of economic efficiency and profit. This is consistent with Bravo and Pinheiro (2005) who identified positive impact of experience on efficiency. From the result, the seven efficiency factors are contained in table 5, credit status and business experience were significant and are evidenced to be related to economic efficiency. Labour and number of employee are negatively signed and significant at 1% probability level. The implication is that labour and number of employee are decreasing with efficiency. Conclusion The study observed that economic efficiency of small and medium scale enterprises varied due to the presence of economic inefficiency effects in production with small scale enterprise been more efficient than medium scale enterprise in the area. This shows that there is a great opportunity for the enterprises to increase their level of efficiency in agribusiness production. There was no significant difference in mean efficiency between the small and medium scale agribusiness enterprises in the area. Access to credit and business experience was found influencing economic efficiency in both small scale and medium scale enterprises. The entrepreneurs are encouraged to adopt cost reducing strategy called vertical integration and government and private sectors should encourage the processors with more credit facilities. References Amaechi, E. C. C. (2007). Capitalization and Efficiency of Smallholder Palm oil Processing Mills in Imo State, Nigeria. PhD. Dissertation, Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike Relative Efficiency of Small and Medium ... 131 Ajibefun, I. A & A. G. Daramola. (2003). Determinants of Technical and Allocative Efficiency of Microenterprises: Firm-level Evidence from Nigeria. Bulletin of African Development Bank, 4, 353-395. Awoke- M. U. & Okorji, E. C. (2003). Analyses of Constraint in Resource Use Efficiency in Multiple Cropping Systems by Small Holder Farmers in Ebonyi state, Nigeria. Global journal of Agric science, 2(2), 132 – 136. Bravo-Ureta & A. E. Pinheiro (2005). Technical, Economic and Allocative Efficiency in Peasant Farming: Evidence from the Dominican Republic. The Developing Economics, xxxv(I), 48-57 CBN (2000). Central Bank of Nigeria Statistical Bulletin, 2(2), December 2000 Coelli, T. J. (1996). A Guide to Frontier 4.1 Computer Program for Stochastic Frontier Productive and Cost Function Estimation. Mimeo, Development of Econometrics University of New England, Arimidate, Australia. Idiong, I. C. (2005). Evaluation of Technical, Allocative and Economic Efficiencies in Rice Production System in Cross River state, Nigeria. A Ph. D Thesis, Submitted to the Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike IFC (2003). Annual Review of Small Business Activities. International Finance Corporation (IFC), Small and Medium Enterprise Development. Library of Congress (2006). Federal Research Division Country Profile; Nigeria P.12. Nwachukwu, I. N. (2006). Economic Efficiency of Vegetable Production in Imo state, Nigeria. Unpublished MSc Dissertation Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike. NCI (2001). Annual Review of Small and Medium Industries in Nigeria. National Council on Industries. Okoye, E. (2006). Efficiency of Small holder Cocoyam Production in Anambra State. An MSc Thesis, Department of Agricultural economics, Michael Okpara University of Agriculture, Umudike. Opara, T. C. (2008). Efficiency and Productivity Growth among Food Crop Farmers in Imo State, Nigeria. An M.sc Thesis Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike Owualah, S. I. (1999). Entrepreneurship in Small Business Firms. GMAG Investments Ltd. (Educational Publishers) Ikeja, Lagos Spilaimen, I. & A. O. Lansink (2005). Learning Inorganic Farming – An application Finishing Dairy Farms. Paper Presented at the Xith Congress of European Association of Agricultural Economics, Copenhagen, Denmark. Aug. 24-27. Sanusi, J. O. (2003). Overview of Government’s Efforts in the Development of SMEs and the Emergence of Small and Medium Industries Equity Investment Scheme (SMIEIS). Paper Presented at a National Summit on SMIEIS Organized by the Banker’s Committee and Lagos Chambers of Commerce and Industry (LCCI), Lagos on 15 th June, 2003. World Bank Survey (1981). Agricultural and the Environmental Perspective for Sustainable Rural Development. Ernst Cutz (Ed) John Hopkins University Press for the World Bank. Tables Table 1: Maximum Likelihood Estimates of the Stochastic Profit Function Model (Translog) for Small Scale Agribusiness Enterprises in Imo State Production factors Parameters Coefficient Standard error t-value Constant term β0 31.750 6.160 5.154*** Wage rate β1 4.904 0.493 9.954*** Price of other inputs β2 -1.459 0.322 -4.528*** Price of petrol β3 -3.401 0.298 -11.415*** Unit of transportation β4 -11.498 4.835 -2.378*** Interest rate β5 -0.270 0.442 -0.612 Asian Journal of Agriculture and Rural Development, 3(3): 127-134 132 Wage rate 2 β6 0.271 0.139 1.955** Price of other inputs 2 β7 0.067 0.221 0.301 Price of petrol 2 β8 0.391 0.359 1.089 Unit cost of transportation 2 Β9 -0.043 0.236 -1.81 Interest rate 2 Β10 0.062 0.015 4.692*** Wage rate x price of other inputs Β11 -1.292 0.916 -1.410 Wage rate x price of petrol Β12 -0.792 0.363 -2.179** Wage cost x unit cost of transport Β13 0.560 0.425 1.318 Wage rate x interest rate Β14 0.107 0.034 3.123*** Price of other inputs x price of petrol Β15 -2.867 0.504 -0.569 Price of other inputs x unit cost of transport Β16 1.664 0.700 2.378** Price of other inputs x interest rate Β17 -0.074 0.062 -1.194 Price of petrol x unit cost of trans Β18 0.071 0.134 0.528 Price of petrol x interest rate Β19 -0.009 0.013 -0.709 Unit cost of transport x interest rate Β20 -0.053 0.017 -3.128*** Diagnostic statistics Log-likelihood function -233.090 Total variance δ 2 0.451 0.044 10.359*** Variance ratio γ 0.983 0.016 60.945*** LR test 49.998 ***,**,* are significant levels at 1.0%, 5% and 10% respectively. Table 2: Maximum Likelihood Estimates of the Stochastic Profit Function Model (Translog) for Medium Scale Agribusiness Enterprises in Imo State Production factors Parameters Coefficient Standard error t-value Constant term β0 91.066 13.562 6.715*** Wage rate β1 15.521 6.548 2.370*** Price of other inputs β2 -8.592 0.617 13.927*** Price of petrol β3 -2.958 0.750 -3.942*** Unit cost of transportation β4 -1.295 0.666 -1.953* Interest rate β5 -0.221 0.735 -0.301 Wage rate 2 β6 3.298 0.197 1.670* Price of other inputs 2 β7 2.357 0.390 2.534** Price of petrol 2 β8 -1.082 0.475 2.280** Unit cost of transportation 2 β9 -0.229 0.213 -1.072 Interest rate 2 β10 0.074 0.019 3.783*** Wage rate x price of other inputs β11 -2.015 2.106 -0.951 Wage rate x price of petrol β12 -1.332 0.992 -1.343 Wage cost x unit cost of transport β13 1.462 0.726 2.013** Wage rate x interest rate β14 -0.097 0.065 -1.505 Price of other inputs x price of petrol β15 3.998 1.206 3.314*** Relative Efficiency of Small and Medium ... 133 Price of other inputs x unit cost of transport β16 -1.785 1.435 -1.244 Price of other inputs x interest rate β17 0.059 0.107 0.555 Price of petrol x unit cost of trans β18 -0.091 0.442 -0.204 Price of petrol x interest rate β19 0.061 0.038 1.619 Unit cost of transport interest rate β20 -0.030 0.024 -1.235 Diagnostic statistics Log-likelihood function -333.083 Total variance δ 2 0.107 0.015 9.343*** Variance ratio γ 0.963 0.041 2.328*** LR test 21.59 Table 3: Maximum Likelihood Estimates of the Determinants of Economic Efficiency of Small Scale Agribusiness Enterprise Variable Parameter Coefficient Standard error t-value Constant Z0 -2.021 3.996 -0.506 Age of enterprise Z1 0.081 0.101 0.745 Labour Z2 -0.000 0.000 -7.537*** Credit status Z3 0.315 0.120 2.619*** Business experience Z4 0.072 0.079 8.867*** Membership to cooperative organization Z5 -2.062 2.116 -0.975 Number of employees Z6 -0.124 0.016 -7.676*** Extension visit Z7 -0.004 0.023 -0.185 ***,**,* are significant levels at 1.0%, 5% and 10% respectively. Table 4: Maximum Likelihood Estimates of the Determinants of Economic Efficiency of Medium Scale Agribusiness Enterprise Variable Parameter Coefficient Standard error t-value Constant Z0 -6.066 9.084 -0.668 Age of enterprise Z1 -0.004 0.023 -0.019 Labour Z2 -0.000 0.000 -3.706*** Credit status Z3 0.660 0.197 3.358*** Business experience Z4 0.048 0.006 7.578*** Membership to cooperative organization Z5 1.509 1.951 0.773 Number of employees Z6 -0.143 0.002 -6.568*** Extension visit Z7 -0.014 -0.033 -0.428 ***,**,* are significant levels at 1.0%, 5% and 10% respectively. Table 5: Frequency Distribution of Economic Efficiency Indices of Small and Medium Scale Agribusiness Enterprises in Imo State Small Medium Economic efficiency index Freq % Freq % 0.00-0.50 72 30.00 77 32.08 0.51-0.60 36 15.00 45 18.75 0.61-0.70 53 22.00 57 23.75 0.71-0.80 51 21.25 51 21.75 Asian Journal of Agriculture and Rural Development, 3(3): 127-134 134 0.81-0.90 23 9.58 7 2.92 0.91-0.99 5 2.08 3 1.25 Total 240 100 240 100 Maximum economic efficiency 0.93 0.91 Minimum economic efficiency 0.09 0.08 Mean economic efficiency 0.57 0.54 Source: computed from output of computer programme frontier 4.1.